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A Course in Machine Learning
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<h2>A Course in Machine Learning</h2>
<h5>by <a href="http://hal3.name/">Hal Daumé III</a></h5>
<hr>

Machine learning is the study of algorithms that learn from data and
experience.  It is applied in a vast variety of application areas,
from medicine to advertising, from military to pedestrian. Any area in
which you need to make sense of data is a potential consumer of
machine learning.<br><br>

CIML is a set of introductory materials that covers most major aspects
of modern machine learning (supervised learning, unsupervised
learning, large margin methods, probabilistic modeling, learning
theory, etc.).  It's focus is on broad applications with a rigorous
backbone.  A subset can be used for an undergraduate course; a
graduate course could probably cover the entire material and then
some.<br><br>

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You may obtain the written materials by <a href="http://www.ciml.info/">purchasing a
($55) print copy</a>, by <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-all.pdf">downloading
the entire book</a>, or by downloading <a href="http://www.ciml.info/#chapters">individual chapters</a> below.  If
you find the electronic version of the book useful and would like
to 
<a href="http://paypal.com/" onclick="document[&#39;donate&#39;].submit(); return false">donate a small amount</a> 
to support further development,
that's always appreciated!
You can get the source code for the book, labs and other teaching
materials on <a href="https://github.com/hal3/ciml/">GitHub</a>.
The current version is 0.99 (the "beta" pre-release).
[You can view <a href="http://www.ciml.info/index_0_9.html">v0.9</a> if you prefer.
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<h5>Support and Mailing Lists:</h5>

If you would like to be informed when new versions of CIML materials
are released, please join the <a href="https://mailman.umiacs.umd.edu/mailman/listinfo/ciml">CIML mailing list</a>.  If
you find errors in the book, please fill out
a <a href="https://github.com/hal3/ciml/issues">bug report</a>.  If you're the first to
submit an error, you'll get listed in the acknowledgments!  <br><br>

<!--

<h5>Code and Datasets:</h5>

Coming soon...<br/><br/>

<h5>Videos:</h5>

Coming soon...<br/><br/>

<h5>Courses Using CIML:</h5>

<ul>
<li> <a href="">Machine Learning</a> @ Bar-Ilan University </li>
<li> <a href="http://www.umiacs.umd.edu/~hal/courses/2011F_ML/">Machine Learning</a> @ University of Maryland </li>
<li> <a href="http://www.cs.utah.edu/~piyush/teaching/cs5350.html">Machine Learning</a> @ University of Utah </li>
<li> <a href="http://stp.ling.uu.se/~nivre/master/ml.html">Machine Learning for NLP</a> @ Uppsala Universitet </li>
<li> <a href="http://www.cse.wustl.edu/~kilian/cse517a2010">Machine Learning</a> @ Washington University in St. Louis </li>
</ul>

Using CIML and not listed here?  <a href="mailto:me AT hal3 DOT name">Email Hal</a>!
<br/><br/>
-->


<a name="chapters"></a><h5>Individual Chapters:</h5>
<ol start="0">
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch00.pdf">Front Matter</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch01.pdf">Decision Trees</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch02.pdf">Limits of Learning</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch03.pdf">Geometry and Nearest Neighbors</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch04.pdf">The Perceptron</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch05.pdf">Practical Issues</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch06.pdf">Beyond Binary Classification</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch07.pdf">Linear Models</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch08.pdf">Bias and Fairness</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch09.pdf">Probabilistic Modeling</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch10.pdf">Neural Networks</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch11.pdf">Kernel Methods</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch12.pdf">Learning Theory</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch13.pdf">Ensemble Methods</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch14.pdf">Efficient Learning</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch15.pdf">Unsupervised Learning</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch16.pdf">Expectation Maximization</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch17.pdf">Structured Prediction</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch18.pdf">Imitation Learning</a> </li>
<li> <a href="http://www.ciml.info/dl/v0_99/ciml-v0_99-ch19.pdf">Back Matter</a> </li>
</ol><br>

<h5>Acknowledgments</h5>

Thanks to everyone who was ever a teacher or student of mine, to those
who provided feedback on drafts, and to colleagues for encouragement
to get this done!  Special thanks to: TODO...
<br><br>


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